SoroushVahidi/scidocs huggingface dataset:
Beam-Steerable Planar Antenna Using Circular Disc and Four PIN-Controlled Tapered Stubs for WiMAX and WLAN Applications
(2016)

A planar beam-steerable antenna aimed at WiMAX and WLAN applications is presented. The design uses a single-layer structure, which includes a central circular disc surrounded by four PIN-controlled tapered microstrip stubs. Using the PIN diodes, the stubs change their status from grounded to open-ended mode to provide pattern reconfigurability in four directions. To support WiMAX and WLAN applications, the antenna is designed to operate at IEEE 802.11b/g standard 2.4 GHz with a bandwidth of 150 MHz in all the four states. A prototype on FR4 substrate with thickness of 6.4 mm and radius of 50 mm is designed and tested. The measured and simulated results of the antenna indicate that the direction of the main beam can be successfully controlled at four specific directions ( φ = 0°, 90 °, 180 °, 270 °), at a deflection angle of 35 ° from the boresight with a stable gain of more than 5 dBi and front-to-back ratio of more than 20 dB across the band 2.38-2.53 GHz.
Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data
(2017)

Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So far, two lines of work have tried to produce a"good" feature vector x from the customer's behavioraldata: (1) application-specific feature engineering using behavioral data and (2) representation learning (such as singular value decomposition or neuralembedding) on behavioral data. Although these approaches successfullyimprove the predictive performance, (1) designing a good feature requiresdomain experts to make a great effort and (2) features obtained fromrepresentation learning are hard to interpret. To overcome these problems, we present a Relational Infinite SupportVector Machine (R-iSVM), a mixture-of-experts model that can leveragebehavioral data. Instead of augmenting the feature vectors of customers, R-iSVM uses behavioral data to find out behaviorally similar customerclusters and constructs a local prediction model at each customer cluster. In doing so, R-iSVM successfully improves the predictive performance withoutrequiring application-specific feature designing and hard-to-interpretrepresentations. Experimental results on three real-world datasets demonstrate the predictiveperformance and interpretability of R-iSVM. Furthermore, R-iSVM can co-existwith previous demographics prediction methods to further improve theirpredictive performance.
Perceptual Design of Haptic Icons
(2003)

The bulk of applications for haptic feedback employ direct rendering approaches wherein a user touches a virtual model of some “real” thing, often displayed graphically as well. We propose a new class of applications based on abstract messages, ranging from “haptic icons” – brief signals conveying an object’s or event’s state, function or content – to an expressive haptic language for interpersonal communication. Building this language requires us to understand how synthetic haptic signals are perceived, and what they can mean to us. Experiments presented here address the perception question by using an efficient version of Multidimensional Scaling (MDS) to extract perceptual axes for complex haptic icons: once this space is mapped, icons can be designed to maximize both differentiability and individual salience. Results show that a set of icons constructed by varying the frequency, magnitude and shape of 2-sec, time-invariant wave shapes map to two perceptual axes, which differ depending on the signals’ frequency range; and suggest that expressive capability is maximized in one frequency subspace.
Accuracy of deception judgments.
(2006)

We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individuals regard their interaction partners as honest. We propose that people judge others' deceptions more harshly than their own and that this double standard in evaluating deceit can explain much of the accumulated literature.
LETOR : Benchmark Dataset for Research on Learning to Rank for Information Retrieval
(2007)

This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that could be used in comparison of existing learning algorithms and in evaluation of newly proposed algorithms, which stood in the way of the related research. To deal with the problem, we have constructed a benchmark dataset referred to as LETOR and distributed it to the research communities. Specifically we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from the datasets, including both conventional features, such as term frequency, inverse document frequency, BM25, and language models for IR, and features proposed recently at SIGIR, such as HostRank, feature propagation, and topical PageRank. We have then packaged LETOR with the extracted features, queries, and relevance judgments. We have also provided the results of several state-ofthe-arts learning to rank algorithms on the data. This paper describes in details about LETOR.
CEET: A Compressed Encrypted & Embedded Technique for Digital Image Steganography
(2014)

In this information era, digital information sharing and transfer plays a vital role and their use has increased exponentially with the development of technology. Thus providing security of data is a topic of concern. Data hiding is a powerful tool that provides mechanism for securing data over insecure channel by concealing information within information. Steganography inherits data hiding concept and passes information through host files in such a way that the existence of the embedded secret is unknown. This paper presents a joint application of compression, encryption and embedding techniques for implementing digital image steganography. Compression technique generates noise in the image. Inorder to retain the noise distortion to a minimum level, LSB insertion method is used for embedding purpose where the bits are inserted at the last 2 LSB’s of the image. In this proposed technique the secret information is initially compressed and then the resultant bits are encrypted. Finally these encrypted bits are embedded into an image. The main objective is to develop an application that increases the steganographic capacity and enhances the stego image quality while keeping the security intact.
Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.
(2011)

Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Preand posttests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners’ insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.
Usability Heuristics for Transactional Web Sites
(2014)

One of the most recognized methods to evaluate usability in software applications is the heuristic evaluation. In this inspection method, Nielsen's heuristics, are the most widely used evaluation instrument. However, there is evidence in the literature which establishes that these heuristics are not appropriate when they are used to measure the level of usability of new emerging categories of software applications. This paper presents a case study where this concept is verified for Transactional Web Sites. Therefore, given the present limitations, a new set of usability heuristics was proposed following a structured and systematic methodology. Finally, fifteen new usability heuristics were obtained as final product of this research. A validation phase allowed to contrast this new proposal with the Nielsen's principles under a real context, where a heuristic evaluation was performed to a Transactional Web Site. The results established that the new set of usability heuristics, which is presented in this study, provides more accurate and promising results than the current proposal of Nielsen.
Knowledge Management Issues in Malaysian Organizations : the Perceptions of Leaders
(2013)

Knowledge management is a relatively young topic to other older strategic tools, and it has been attracting the attention of researchers and academics over the past decade or so. In this regard globalization is already in many countries including Malaysia like many other developing countries, Malaysia is also striving to fulfil its vision of becoming a developed nation. It is a fast growing country in terms of both economy and technology. However, obstacles for successful knowledge management initiatives will be there for this country, as well. Thus, exploration of the potential barriers is essential before any Malaysian company embarks on a knowledge management journey. This research has shed light to some of the important issues and challenges with reference to knowledge management in Malaysia by providing a more thorough and clear picture of the knowledge management status amongst Malaysian organizations. This could in turn help top managers and decision makers to develop a more insightful agenda to ensure success for their respective knowledge management initiatives.
Generating Extractive Summaries of Scientific Paradigms
(2013)

Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scientific topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.
Muscular and postural synergies of the human hand.
(2004)

Because humans have limited ability to independently control the many joints of the hand, a wide variety of hand shapes can be characterized as a weighted combination of just two or three main patterns of covariation in joint rotations, or "postural synergies." The present study sought to align muscle synergies with these main postural synergies and to describe the form of membership of motor units in these postural/muscle synergies. Seventeen joint angles and the electromyographic (EMG) activities of several hand muscles (both intrinsic and extrinsic muscles) were recorded while human subjects held the hand statically in 52 specific shapes (i.e., shaping the hand around 26 commonly grasped objects or forming the 26 letter shapes of a manual alphabet). Principal-components analysis revealed several patterns of muscle synergy, some of which represented either coactivation of all hand muscles, or reciprocal patterns of activity (above and below average levels) in the intrinsic index finger and thumb muscles or (to a lesser extent) in the extrinsic four-tendoned extensor and flexor muscles. Single- and multiunit activity was generally a multimodal function of whole hand shape. This implies that motor-unit activation does not align with a single synergy; instead, motor units participate in multiple muscle synergies. Thus it appears that the organization of the global pattern of hand muscle activation is highly distributed. This organization mirrors the highly fractured somatotopy of cortical hand representations and may provide an ideal substrate for motor learning and recovery from injury.
IT Governance Maturity: Developing a Maturity Model Using the Delphi Method
(2015)

To advance in maturity, organizations should pay attention to both the hard and soft sides of IT governance (ITG). The hard side is related to processes and structure, the soft side to social aspects like behavior and organizational culture. This paper describes a study to develop an ITG maturity model (MM) that includes both. Our research method is based on literature study, the Delphi method and makes use of a Group Decision Support System. We chose to design a focus area MM. In this type of MM maturity is determined by a set of focus areas. The study reveals one MM as being appropriate for hard ITG. For soft ITG we found no single model appropriate. Soft governance needs more specific capabilities defined for each focus area individually. Based on knowledge from literature and experts we selected models for each focus area. Three alternatives for informal organization need further research.
A maturity model for information governance
(2016)

Information Governance (IG) as defined by Gartner is the “specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. Includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals”. In this paper, we present how to create an IG maturity model based on existing reference documents. The process is based on existing maturity model development methods. These methods allow for a systematic approach to maturity model development backed up by a well-known and proved scientific research method called Design Science Research. Then, based on the maturity model proposed in this paper, an assessment is conducted and the results are presented, this assessment was conducted as a self-assessment in the context of the EC-funded E-ARK project for the seven pilots of the project. The main conclusion from this initial assessment is that there is much room for improvement with most pilots achieving results between maturity level two and three. As future work, the goal is to analyze other references from different domains, such as, records management. These references will enhance, detail and help develop the maturity model making it even more valuable for all types of organization that deal with information governance.
A Model of Data Warehousing Process Maturity
(2012)

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.
3D Reconstruction from Hyperspectral Images
(2015)

3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method to reconstruct a 3D model from hyper spectral images. Our proposed method first generates 3D point sets from images at each wavelength using the typical structure from motion approach. A structural descriptor is developed to characterize the spatial relationship between the points, which allows robust point matching between two 3D models at different wavelength. Then a 3D registration method is introduced to combine all band-level models into a single and complete hyper spectral 3D model. As far as we know, this is the first attempt in reconstructing a complete 3D model from hyper spectral images. This work allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications.
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
(2018)

Neural networks have great success in many machine learning applications, but the fundamental learning theory behind them remains largely unsolved. Learning neural networks is NP-hard, but in practice, simple algorithms like stochastic gradient descent (SGD) often produce good solutions. Moreover, it is observed that overparameterization (that is, designing networks whose number of parameters is larger than statistically needed to perfectly fit the data) improves both optimization and generalization, appearing to contradict traditional learning theory. In this work, we extend the theoretical understanding of two and three-layer neural networks in the overparameterized regime. We prove that, using overparameterized neural networks, one can (improperly) learn some notable hypothesis classes, including two and three-layer neural networks with fewer parameters. Moreover, the learning process can be simply done by SGD or its variants in polynomial time using polynomially many samples. We also show that for a fixed sample size, the generalization error of the solution found by some SGD variant can be made almost independent of the number of parameters in the overparameterized network. ∗V1 appears on this date and V2 polishes writing and tightens parameters. Authors sorted in alphabetical order.
A novel compact ka-band high-rejection diplexer based on substrate integrated waveguide
(2012)

In this paper, a compact diplexer using substrate integrated waveguide (SIW) resonators is introduced. The diplexer consists of two filters and a circulator. The diplexer channels are 4% and 5% relative bandwidth at 32GHz and 33GHz, respectively. The filters are based on SIW resonators, which have higher Q values. This paper also analyzed the factors which affect the Q value of resonators, and describes the designing procedure of Generalized Chebyshev filters, which used in this paper. The circulator also is on account of SIW. It uses inductive windows to increase bandwidth. At last, the paper gives the diplexer's simulation and measure results. And two results are similar with each other.
B2B E-commerce: Frameworks for e-readiness assessment
(2008)

This paper looks into the development of e- readiness model for B2B e-commerce of Small and Medium Enterprises (SMEs). Drawing on existing research on e-readiness models of ICT (information, Communications Technology) specifically B2B e-commerce technologies, this paper formulates a conceptual framework of B2B e-commerce assessment constructs together with the specific research approach towards the development of a more robust e-readiness model. The research presents a conceptual model and framework that highlight the key factors in B2B e-commerce implementation, index score formulations, focus group evaluations and the recommendation approaches to improve B2B e-commerce readiness.
Hopping of a monopedal robot with a biarticular muscle driven by electromagnetic linear actuators
(2012)

The compliance of muscles with external forces and the structural stability given by biarticular muscles are important features of animals to realize dynamic whole body motions such as running and hopping in various environments. For this reason, we have been studying an electromagnetic linear actuator. This actuator can emulate the behavior of a human muscle such as the spring-damper characteristics by quick control of the output force (i.e. impedance control) and it is expected to be used as an artificial muscle. In this paper, we develop a monopedal robot possessing bi- and mono-articular muscles implemented by linear actuators. Thanks to the biarticular muscle, the bouncing direction of the robot can be controlled by changing the stiffness ellipse at the endpoint (i.e. foot) of the robot. We confirm that the bouncing direction of the robot can be controlled and hopping can be achieved by changing the stiffness ellipse.